3D_Appearance_SR

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3D_Appearance_SR

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This is the official website of our work 3D Appearance Super-Resolution with Deep Learning published on CVPR2019.

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We received **Best Poster Prize** at [ICVSS 2019](https://iplab.dmi.unict.it/icvss2019/PresentationPrize).

This is the official website of our work [3D Appearance Super-Resolution with Deep Learning](./code/scripts/3d_appearance_sr.pdf) [(arxiv)](https://arxiv.org/abs/1906.00925) published on CVPR2019.

We provided 3DASR, a 3D appearance SR dataset that captures both synthetic and real scenes with a large variety of texture characteristics. The dataset contains ground truth HR texture maps and LR texture maps of scaling factors ×2, ×3, and ×4. The 3D mesh, multi-view images, projection matrices, and normal maps are also provided. We introduced a deep learning-based SR framework in the multi-view setting. We showed that 2D deep learning-based SR techniques can successfully be adapted to the new texture domain by introducing the geometric information via normal maps.

![alt text][contribution]

[contribution]: ./code/scripts/contribution.jpg "We introduce the 3DASR, a 3D appearance SR dataset and a deep learning-based approach to super-resolve the appearance of 3D objects."
We introduce the 3DASR, a 3D appearance SR dataset and a deep learning-based approach to super-resolve the appearance of 3D objects.

# Dependencies
* Python 3.6
* PyTorch >= 1.0.0
* numpy
* skimage
* imageio
* matplotlib
* tqdm

# Quick Start (Test)
1. `git clone https://github.com/ofsoundof/3D_Appearance_SR.git`
2. Download pretrained model and texture map dataset.
3. Put pretrained model at this website ➡ [`./experiment/`](./experiment).
4. `cd ./code/script`
   
      `CUDA_VISIBLE_DEVICES=xx python ../main.py --model FINETUNE --submodel NLR --save Test/NLR_first --scale 4 --n_resblocks 32 --n_feats 256 --res_scale 0.1 --pre_train ../../experiment/model/NLR/model_x2_split1.pt  --data_train texture --data_test texture --model_one one --subset . --normal_lr lr --input_res lr --chop --reset --save_results --print_model --test_only`
   
   Use `--ext sep_reset` for the first run that uses a specific split of the two splits from cross-validation.
   
   Be sure to change log directory `--dir` and data directory `--dir_data`.
   
# How to Run the Code
## Prepare pretrained model
1. Download our pretrained model for 3D appearance SR from [Releases/v1.0.0](https://github.com/ofsoundof/3D_Appearance_SR/releases/tag/v1.0.0) or [BaiduNetDisk, extraction code: nnnm](https://pan.baidu.com/s/1-_yozGa3QMMe0TRIUg5WBw). The pretrained models of NLR and NHR in the paper are included.

2. Download the pretrained EDSR model from [EDSR project page](https://github.com/thstkdgus35/EDSR-PyTorch).

3. Put the pretrained model at [`./experiment`](./experiment).

## Prepare dataset
1. Download the texture map of the 3D appearance dataset from [Releases/v1.0.0](https://github.com/ofsoundof/3D_Appearance_SR/releases/tag/v1.0.0) or [BaiduNetDisk, extraction code: crnw](https://pan.baidu.com/s/1U-bnnG6LjOVtHqX3fMCq2w).

## Train and test
1. Please refer to [`demo.sh`](./code/scripts/demo.sh) for the training and testing demo script. In a batch system, you can also use [`qsub_NLR.sh`](./code/scripts/qsub_NLR.sh).
2. Remember to change the log directory `--dir` and data directory `--dir_data`. `--dir` is the directory where you can put your log information and the trained model. `--dir_data` is the directory where you put the dataset.

# BibTeX
If you find our work useful in your research or publication, please cite our work:

Yawei Li , Vagia Tsiminaki, Radu Timofte, Marc Pollefeys, and Luc van Gool, "**3D Appearance Super-Resolution with Deep Learning**" In *Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition*, 2019.

@inproceedings{li2019_3dappearance,
      title={3D Appearance Super-Resolution with Deep Learning},
      author={Li, Yawei and Tsiminaki, Vagia and Timofte, Radu and Pollefeys, Marc and Van Gool, Luc},
      booktitle={In Proceedings of the IEEE International Conference on Computer Vision},
      year={2019}
    }

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